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524 lines
17 KiB
C++
524 lines
17 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000, Intel Corporation, all rights reserved.
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// Copyright (C) 2016, Itseez Inc, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "precomp.hpp"
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#include "limits"
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#include <iostream>
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using std::cout;
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using std::endl;
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/****************************************************************************************\
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* Stochastic Gradient Descent SVM Classifier *
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\****************************************************************************************/
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namespace cv
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{
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namespace ml
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{
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class SVMSGDImpl CV_FINAL : public SVMSGD
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{
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public:
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SVMSGDImpl();
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virtual ~SVMSGDImpl() {}
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virtual bool train(const Ptr<TrainData>& data, int) CV_OVERRIDE;
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virtual float predict( InputArray samples, OutputArray results=noArray(), int flags = 0 ) const CV_OVERRIDE;
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virtual bool isClassifier() const CV_OVERRIDE;
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virtual bool isTrained() const CV_OVERRIDE;
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virtual void clear() CV_OVERRIDE;
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virtual void write(FileStorage &fs) const CV_OVERRIDE;
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virtual void read(const FileNode &fn) CV_OVERRIDE;
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virtual Mat getWeights() CV_OVERRIDE { return weights_; }
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virtual float getShift() CV_OVERRIDE { return shift_; }
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virtual int getVarCount() const CV_OVERRIDE { return weights_.cols; }
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virtual String getDefaultName() const CV_OVERRIDE {return "opencv_ml_svmsgd";}
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virtual void setOptimalParameters(int svmsgdType = ASGD, int marginType = SOFT_MARGIN) CV_OVERRIDE;
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inline int getSvmsgdType() const CV_OVERRIDE { return params.svmsgdType; }
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inline void setSvmsgdType(int val) CV_OVERRIDE { params.svmsgdType = val; }
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inline int getMarginType() const CV_OVERRIDE { return params.marginType; }
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inline void setMarginType(int val) CV_OVERRIDE { params.marginType = val; }
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inline float getMarginRegularization() const CV_OVERRIDE { return params.marginRegularization; }
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inline void setMarginRegularization(float val) CV_OVERRIDE { params.marginRegularization = val; }
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inline float getInitialStepSize() const CV_OVERRIDE { return params.initialStepSize; }
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inline void setInitialStepSize(float val) CV_OVERRIDE { params.initialStepSize = val; }
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inline float getStepDecreasingPower() const CV_OVERRIDE { return params.stepDecreasingPower; }
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inline void setStepDecreasingPower(float val) CV_OVERRIDE { params.stepDecreasingPower = val; }
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inline cv::TermCriteria getTermCriteria() const CV_OVERRIDE { return params.termCrit; }
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inline void setTermCriteria(const cv::TermCriteria& val) CV_OVERRIDE { params.termCrit = val; }
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private:
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void updateWeights(InputArray sample, bool positive, float stepSize, Mat &weights);
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void writeParams( FileStorage &fs ) const;
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void readParams( const FileNode &fn );
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static inline bool isPositive(float val) { return val > 0; }
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static void normalizeSamples(Mat &matrix, Mat &average, float &multiplier);
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float calcShift(InputArray _samples, InputArray _responses) const;
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static void makeExtendedTrainSamples(const Mat &trainSamples, Mat &extendedTrainSamples, Mat &average, float &multiplier);
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// Vector with SVM weights
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Mat weights_;
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float shift_;
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// Parameters for learning
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struct SVMSGDParams
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{
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float marginRegularization;
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float initialStepSize;
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float stepDecreasingPower;
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TermCriteria termCrit;
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int svmsgdType;
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int marginType;
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};
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SVMSGDParams params;
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};
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Ptr<SVMSGD> SVMSGD::create()
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{
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return makePtr<SVMSGDImpl>();
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}
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Ptr<SVMSGD> SVMSGD::load(const String& filepath, const String& nodeName)
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{
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return Algorithm::load<SVMSGD>(filepath, nodeName);
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}
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void SVMSGDImpl::normalizeSamples(Mat &samples, Mat &average, float &multiplier)
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{
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int featuresCount = samples.cols;
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int samplesCount = samples.rows;
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average = Mat(1, featuresCount, samples.type());
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CV_Assert(average.type() == CV_32FC1);
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for (int featureIndex = 0; featureIndex < featuresCount; featureIndex++)
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{
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average.at<float>(featureIndex) = static_cast<float>(mean(samples.col(featureIndex))[0]);
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}
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for (int sampleIndex = 0; sampleIndex < samplesCount; sampleIndex++)
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{
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samples.row(sampleIndex) -= average;
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}
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double normValue = norm(samples);
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multiplier = static_cast<float>(sqrt(static_cast<double>(samples.total())) / normValue);
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samples *= multiplier;
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}
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void SVMSGDImpl::makeExtendedTrainSamples(const Mat &trainSamples, Mat &extendedTrainSamples, Mat &average, float &multiplier)
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{
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Mat normalizedTrainSamples = trainSamples.clone();
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int samplesCount = normalizedTrainSamples.rows;
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normalizeSamples(normalizedTrainSamples, average, multiplier);
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Mat onesCol = Mat::ones(samplesCount, 1, CV_32F);
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cv::hconcat(normalizedTrainSamples, onesCol, extendedTrainSamples);
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}
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void SVMSGDImpl::updateWeights(InputArray _sample, bool positive, float stepSize, Mat& weights)
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{
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Mat sample = _sample.getMat();
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int response = positive ? 1 : -1; // ensure that trainResponses are -1 or 1
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if ( sample.dot(weights) * response > 1)
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{
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// Not a support vector, only apply weight decay
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weights *= (1.f - stepSize * params.marginRegularization);
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}
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else
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{
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// It's a support vector, add it to the weights
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weights -= (stepSize * params.marginRegularization) * weights - (stepSize * response) * sample;
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}
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}
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float SVMSGDImpl::calcShift(InputArray _samples, InputArray _responses) const
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{
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float margin[2] = { std::numeric_limits<float>::max(), std::numeric_limits<float>::max() };
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Mat trainSamples = _samples.getMat();
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int trainSamplesCount = trainSamples.rows;
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Mat trainResponses = _responses.getMat();
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CV_Assert(trainResponses.type() == CV_32FC1);
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for (int samplesIndex = 0; samplesIndex < trainSamplesCount; samplesIndex++)
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{
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Mat currentSample = trainSamples.row(samplesIndex);
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float dotProduct = static_cast<float>(currentSample.dot(weights_));
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bool positive = isPositive(trainResponses.at<float>(samplesIndex));
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int index = positive ? 0 : 1;
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float signToMul = positive ? 1.f : -1.f;
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float curMargin = dotProduct * signToMul;
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if (curMargin < margin[index])
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{
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margin[index] = curMargin;
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}
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}
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return -(margin[0] - margin[1]) / 2.f;
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}
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bool SVMSGDImpl::train(const Ptr<TrainData>& data, int)
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{
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clear();
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CV_Assert( isClassifier() ); //toDo: consider
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Mat trainSamples = data->getTrainSamples();
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int featureCount = trainSamples.cols;
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Mat trainResponses = data->getTrainResponses(); // (trainSamplesCount x 1) matrix
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CV_Assert(trainResponses.rows == trainSamples.rows);
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if (trainResponses.empty())
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{
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return false;
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}
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int positiveCount = countNonZero(trainResponses >= 0);
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int negativeCount = countNonZero(trainResponses < 0);
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if ( positiveCount <= 0 || negativeCount <= 0 )
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{
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weights_ = Mat::zeros(1, featureCount, CV_32F);
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shift_ = (positiveCount > 0) ? 1.f : -1.f;
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return true;
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}
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Mat extendedTrainSamples;
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Mat average;
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float multiplier = 0;
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makeExtendedTrainSamples(trainSamples, extendedTrainSamples, average, multiplier);
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int extendedTrainSamplesCount = extendedTrainSamples.rows;
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int extendedFeatureCount = extendedTrainSamples.cols;
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Mat extendedWeights = Mat::zeros(1, extendedFeatureCount, CV_32F);
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Mat previousWeights = Mat::zeros(1, extendedFeatureCount, CV_32F);
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Mat averageExtendedWeights;
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if (params.svmsgdType == ASGD)
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{
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averageExtendedWeights = Mat::zeros(1, extendedFeatureCount, CV_32F);
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}
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RNG rng(0);
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CV_Assert (params.termCrit.type & TermCriteria::COUNT || params.termCrit.type & TermCriteria::EPS);
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int maxCount = (params.termCrit.type & TermCriteria::COUNT) ? params.termCrit.maxCount : INT_MAX;
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double epsilon = (params.termCrit.type & TermCriteria::EPS) ? params.termCrit.epsilon : 0;
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double err = DBL_MAX;
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CV_Assert (trainResponses.type() == CV_32FC1);
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// Stochastic gradient descent SVM
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for (int iter = 0; (iter < maxCount) && (err > epsilon); iter++)
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{
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int randomNumber = rng.uniform(0, extendedTrainSamplesCount); //generate sample number
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Mat currentSample = extendedTrainSamples.row(randomNumber);
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float stepSize = params.initialStepSize * std::pow((1 + params.marginRegularization * params.initialStepSize * (float)iter), (-params.stepDecreasingPower)); //update stepSize
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updateWeights( currentSample, isPositive(trainResponses.at<float>(randomNumber)), stepSize, extendedWeights );
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//average weights (only for ASGD model)
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if (params.svmsgdType == ASGD)
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{
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averageExtendedWeights = ((float)iter/ (1 + (float)iter)) * averageExtendedWeights + extendedWeights / (1 + (float) iter);
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err = norm(averageExtendedWeights - previousWeights);
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averageExtendedWeights.copyTo(previousWeights);
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}
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else
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{
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err = norm(extendedWeights - previousWeights);
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extendedWeights.copyTo(previousWeights);
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}
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}
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if (params.svmsgdType == ASGD)
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{
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extendedWeights = averageExtendedWeights;
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}
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Rect roi(0, 0, featureCount, 1);
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weights_ = extendedWeights(roi);
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weights_ *= multiplier;
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CV_Assert((params.marginType == SOFT_MARGIN || params.marginType == HARD_MARGIN) && (extendedWeights.type() == CV_32FC1));
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if (params.marginType == SOFT_MARGIN)
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{
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shift_ = extendedWeights.at<float>(featureCount) - static_cast<float>(weights_.dot(average));
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}
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else
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{
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shift_ = calcShift(trainSamples, trainResponses);
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}
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return true;
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}
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float SVMSGDImpl::predict( InputArray _samples, OutputArray _results, int ) const
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{
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float result = 0;
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cv::Mat samples = _samples.getMat();
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int nSamples = samples.rows;
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cv::Mat results;
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CV_Assert( samples.cols == weights_.cols && samples.type() == CV_32FC1);
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if( _results.needed() )
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{
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_results.create( nSamples, 1, samples.type() );
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results = _results.getMat();
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}
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else
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{
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CV_Assert( nSamples == 1 );
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results = Mat(1, 1, CV_32FC1, &result);
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}
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for (int sampleIndex = 0; sampleIndex < nSamples; sampleIndex++)
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{
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Mat currentSample = samples.row(sampleIndex);
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float criterion = static_cast<float>(currentSample.dot(weights_)) + shift_;
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results.at<float>(sampleIndex) = (criterion >= 0) ? 1.f : -1.f;
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}
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return result;
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}
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bool SVMSGDImpl::isClassifier() const
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{
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return (params.svmsgdType == SGD || params.svmsgdType == ASGD)
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&&
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(params.marginType == SOFT_MARGIN || params.marginType == HARD_MARGIN)
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&&
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(params.marginRegularization > 0) && (params.initialStepSize > 0) && (params.stepDecreasingPower >= 0);
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}
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bool SVMSGDImpl::isTrained() const
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{
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return !weights_.empty();
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}
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void SVMSGDImpl::write(FileStorage& fs) const
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{
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if( !isTrained() )
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CV_Error( CV_StsParseError, "SVMSGD model data is invalid, it hasn't been trained" );
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writeFormat(fs);
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writeParams( fs );
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fs << "weights" << weights_;
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fs << "shift" << shift_;
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}
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void SVMSGDImpl::writeParams( FileStorage& fs ) const
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{
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String SvmsgdTypeStr;
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switch (params.svmsgdType)
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{
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case SGD:
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SvmsgdTypeStr = "SGD";
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break;
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case ASGD:
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SvmsgdTypeStr = "ASGD";
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break;
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default:
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SvmsgdTypeStr = format("Unknown_%d", params.svmsgdType);
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}
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fs << "svmsgdType" << SvmsgdTypeStr;
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String marginTypeStr;
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switch (params.marginType)
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{
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case SOFT_MARGIN:
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marginTypeStr = "SOFT_MARGIN";
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break;
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case HARD_MARGIN:
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marginTypeStr = "HARD_MARGIN";
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break;
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default:
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marginTypeStr = format("Unknown_%d", params.marginType);
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}
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fs << "marginType" << marginTypeStr;
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fs << "marginRegularization" << params.marginRegularization;
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fs << "initialStepSize" << params.initialStepSize;
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fs << "stepDecreasingPower" << params.stepDecreasingPower;
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fs << "term_criteria" << "{:";
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if( params.termCrit.type & TermCriteria::EPS )
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fs << "epsilon" << params.termCrit.epsilon;
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if( params.termCrit.type & TermCriteria::COUNT )
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fs << "iterations" << params.termCrit.maxCount;
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fs << "}";
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}
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void SVMSGDImpl::readParams( const FileNode& fn )
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{
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String svmsgdTypeStr = (String)fn["svmsgdType"];
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int svmsgdType =
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svmsgdTypeStr == "SGD" ? SGD :
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svmsgdTypeStr == "ASGD" ? ASGD : -1;
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if( svmsgdType < 0 )
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CV_Error( CV_StsParseError, "Missing or invalid SVMSGD type" );
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params.svmsgdType = svmsgdType;
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String marginTypeStr = (String)fn["marginType"];
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int marginType =
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marginTypeStr == "SOFT_MARGIN" ? SOFT_MARGIN :
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marginTypeStr == "HARD_MARGIN" ? HARD_MARGIN : -1;
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if( marginType < 0 )
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CV_Error( CV_StsParseError, "Missing or invalid margin type" );
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params.marginType = marginType;
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CV_Assert ( fn["marginRegularization"].isReal() );
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params.marginRegularization = (float)fn["marginRegularization"];
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CV_Assert ( fn["initialStepSize"].isReal() );
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params.initialStepSize = (float)fn["initialStepSize"];
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CV_Assert ( fn["stepDecreasingPower"].isReal() );
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params.stepDecreasingPower = (float)fn["stepDecreasingPower"];
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FileNode tcnode = fn["term_criteria"];
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CV_Assert(!tcnode.empty());
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params.termCrit.epsilon = (double)tcnode["epsilon"];
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params.termCrit.maxCount = (int)tcnode["iterations"];
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params.termCrit.type = (params.termCrit.epsilon > 0 ? TermCriteria::EPS : 0) +
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(params.termCrit.maxCount > 0 ? TermCriteria::COUNT : 0);
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CV_Assert ((params.termCrit.type & TermCriteria::COUNT || params.termCrit.type & TermCriteria::EPS));
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}
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void SVMSGDImpl::read(const FileNode& fn)
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{
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clear();
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readParams(fn);
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fn["weights"] >> weights_;
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fn["shift"] >> shift_;
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}
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void SVMSGDImpl::clear()
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{
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weights_.release();
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shift_ = 0;
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}
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SVMSGDImpl::SVMSGDImpl()
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{
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clear();
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setOptimalParameters();
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}
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void SVMSGDImpl::setOptimalParameters(int svmsgdType, int marginType)
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{
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switch (svmsgdType)
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{
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case SGD:
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params.svmsgdType = SGD;
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params.marginType = (marginType == SOFT_MARGIN) ? SOFT_MARGIN :
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(marginType == HARD_MARGIN) ? HARD_MARGIN : -1;
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params.marginRegularization = 0.0001f;
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params.initialStepSize = 0.05f;
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params.stepDecreasingPower = 1.f;
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params.termCrit = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 100000, 0.00001);
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break;
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case ASGD:
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params.svmsgdType = ASGD;
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params.marginType = (marginType == SOFT_MARGIN) ? SOFT_MARGIN :
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(marginType == HARD_MARGIN) ? HARD_MARGIN : -1;
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params.marginRegularization = 0.00001f;
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params.initialStepSize = 0.05f;
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params.stepDecreasingPower = 0.75f;
|
|
params.termCrit = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 100000, 0.00001);
|
|
break;
|
|
|
|
default:
|
|
CV_Error( CV_StsParseError, "SVMSGD model data is invalid" );
|
|
}
|
|
}
|
|
} //ml
|
|
} //cv
|